A Bayesian optimization approach has been proposed recently for the optimization problems involving the evaluations of black-box functions with high computational cost in either objective functions or ...

This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online ...

This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible.

To address this problem, we propose an efficient EM optimization technique with a novel parallel local sampling strategy and Bayesian optimization (BO) for microwave applications in this article. We ...

BoTorch is a library built on top of PyTorch for Bayesian Optimization. It combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, ...

The pair is also used in optimising hyperparameters for an ML model and the process is known as Bayesian Optimization. In this article, we will learn to implement Bayesian Optimization to find optimal ...

In summary, the traditional one-dimensional optimization method is inadequate for investigating the muon generation process. In this paper, we combine Latin hypercube sampling (LHS) with Bayesian ...